Reflection Publishes 156-Page Beam Technical Report: 23.8T-Token Pretraining, 4-Week Async RL on 10.5K GB300s, NVFP4 Keeps ~99% of Scores
On 2026-11-06 Reflection's official account released the Beam model technical report (a 156-page PDF), detailing how its 501.3B-total / 23.4B-active open-weight MoE was trained: 23.8T pretraining tokens on 6,144 GB300 GPUs in under four weeks, then a fully asynchronous RL run on about 10.5K GB300s for four weeks that produced over 100M rollouts. The report says FP8 and NVFP4 checkpoints keep 99.93% and 98.98% of median benchmark scores, and the 1.93B DFlash 2 drafter speeds single-request decoding 1.96–3.03x (vendor figures).
Key Takeaways
- Scale: 501.3B total / 23.4B active, 52 layers, 256 experts per layer with 8 active per token, 151,936-token vocabulary
- Pretraining: 23.8T tokens on 6,144 GB300s in under four weeks, 92.3% goodput late in the run and 21% steady-state MFU
- RL: about 10.5K GB300s for four weeks, 100M+ rollouts, 110K concurrent rollouts and 12-second weight updates; the report sees no plateau as RL compute grows
- Serving: FP8 / NVFP4 keep 99.93% / 98.98% of median scores; DFlash 2 speeds single-request decoding 1.96-3.03x
- Safety: 88.9% refuse/pivot on harmful requests and 81.5% compliance on harmless ones in in-house ISE; Limited in all four systemic-risk categories
Linked Models & Agents:
⚡ beam🌐 reflection
